{"id":2144199,"url":"https://alion.io/job/beacon-ai-senior-software-engineer-cloud-infrastructure","title":"Senior Software Engineer, Cloud Infrastructure","company":{"id":3354,"name":"Beacon AI","domain":"beaconai.com","url":"https://alion.io/company/beaconai","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["San Carlos, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":173000,"max":225000,"currency":"USD","period":"year","gross":null,"usd_annual":225000},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"LLM","optional":false},{"name":"A/B Testing","optional":true},{"name":"Amazon Aurora","optional":true},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon ECS","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon EventBridge","optional":true},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS CDK","optional":true},{"name":"AWS Lambda","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"CI/CD","optional":true},{"name":"DynamoDB","optional":true},{"name":"Embeddings","optional":true},{"name":"Function Calling","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"IAM","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"Least Privilege","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"OpenSearch","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Python","optional":true},{"name":"Quantization","optional":true},{"name":"RAG","optional":true},{"name":"Redis","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Terraform","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"Triton Inference Server","optional":true}],"status":"live","first_seen_at":"2026-10-02T23:35:50Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-12T02:58:23Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"About Beacon AI\nWe’re a fast-moving team of aviators, engineers, and operators building an AI platform to make flying safer, more efficient, and more capable. Backed by top investors, we’ve secured a dozen Department of Defense contracts and partnered with major airlines to deliver mission-critical systems. We operate without silos or heavy processes. Small, focused teams own what they build, ship quickly, and learn fast, pushing the boundaries of how humans and AI work together in aviation.\nRole Overview\nWe are seeking skilled Senior Software Engineers, Cloud Infrastructure to lead the buildout of our AWS foundation and our LLM platform. You will design, implement, and operate services that are scalable, reliable, and secure.\nThe broad scope means focus areas in LLM/ML Infra and IoT infra are strong bonus points. For ML Infra, build the stack that powers retrieval-augmented generation and application workflows built with frameworks like LangChain. Experience with IoT AWS services is a plus.\nYou will work closely with other engineers and product management. The ideal candidate is hands-on, comfortable with ambiguity, and excited to build from first principles.\nKey Responsibilities\nCloud Infrastructure Setup and Maintenance\nDesign, provision, and maintain AWS infrastructure using IaC tools such as AWS CDK or Terraform.\n\nBuild CI/CD and testing for apps, infra, and ML pipelines using GitHub Actions, CodeBuild, and CodePipeline.\n\nOperate secure networking with VPCs, PrivateLink, and VPC endpoints. Manage IAM, KMS, Secrets Manager, and audit logging.\n\nLLM Platform and Runtime\nStand up and operate model endpoints using AWS Bedrock and/or SageMaker; evaluate when to use ECS/EKS, Lambda, or Batch for inference jobs.\n\nBuild and maintain application services that call LLMs through clean APIs, with streaming, batching, and backoff strategies.\n\nImplement prompt and tool execution flows with LangChain or similar, including agent tools and function calling.\n\nRAG Data Systems and Vector Search\nDesign chunking and embedding pipelines for documents, time series, and multimedia. Orchestrate with Step Functions or Airflow.\n\nOperate vector search using OpenSearch Serverless, Aurora PostgreSQL with pgvector, or Pinecone. Tune recall, latency, and cost.\n\nBuild and maintain knowledge bases and data syncs from S3, Aurora, DynamoDB, and external sources.\n\nEvaluation, Observability, and Cost Governance\nCreate offline and online eval harnesses for prompts, retrievers, and chains. Track quality, latency, and regression risk.\n\nInstrument model and app telemetry with CloudWatch and OpenTelemetry. Build token usage and cost dashboards with budgets and alerts.\n\nAdd guardrails, rate limits, fallbacks, and provider routing for resilience.\n\nSafety, Privacy, and Compliance\nImplement PII detection and redaction, access controls, content filters, and human-in-the-loop review where needed.\n\nUse Bedrock Guardrails or policy services to enforce safety standards. Maintain audit trails for regulated environments.\n\nData Pipeline Construction\nBuild ingestion and processing pipelines for structured, unstructured, and multimedia data. Ensure integrity, lineage, and cataloging with Glue and Lake Formation.\n\nOptimize bulk data movement and storage in S3, Glacier, and tiered storage. Use Athena for ad-hoc analysis.\n\nIoT Deployment Management\nManage infrastructure that deploys to and communicates with edge devices. Support secure messaging, identity, and over-the-air updates.\n\nAnalytics and Application Support\nPartner with product and application teams to integrate retrieval services, embeddings, and LLM chains into user-facing features.\n\nProvide expert troubleshooting for cloud and ML services with an emphasis on uptime and performance.\n\nPerformance Optimization\nTune retrieval quality, context window use, and caching with Redis or Bedrock Knowledge Bases.\n\nOptimize inference with model selection, quantization where applicable, GPU/CPU instance choices, and autoscaling strategies.\n\nWhat Will Make You Successful\nEnd-to-End Ownership: Drives work from design through production, including on-call and continuous improvement.\n\nLLM Systems Experience: Shipped or operated LLM-powered applications in production. Familiar with RAG design, prompt versioning, and chain orchestration using LangChain or similar.\n\nAWS Depth: Strong with core AWS services such as VPC, IAM, KMS, CloudWatch, S3, ECS/EKS, Lambda, Step Functions, Bedrock, and SageMaker.\n\nData Engineering Skills: Comfortable building ingestion and transformation pipelines in Python. Familiar with Glue, Athena, and event-driven patterns using EventBridge and SQS.\n\nSecurity Mindset: Applies least privilege, secrets management, network isolation, and compliance practices appropriate to sensitive data.\n\nEvaluation and Metrics: Uses quantitative evals, A/B testing, and live metrics to guide improvements.\n\nClear Communication: Explains tradeoffs and aligns partners across product, security, and application engineering.\n\nBonus Points\n4+ years working with serverless or container platforms on AWS.\n\nExperience with vector databases, OpenSearch, or pgvector at scale.\n\nHands-on with Bedrock Guardrails, Knowledge Bases, or custom policy engines.\n\nFamiliarity with GPU workloads, Triton Inference Server, or TensorRT-LLM.\n\nExperience with big data tools for large-scale processing and search.\n\nBackground in aviation data or other safety-critical domains.\n\nDevOps or DevSecOps experience automating CI/CD for ML and app services.\n\nWork Location\n This is a hybrid role based in San Carlos, CA, with 3+ days per week onsite and the option to work remotely on remaining days.\nPerks & Benefits (Full-Time Employees)\nHealthcare: 100%* of employee medical premiums covered; 25% for dependents\n\nTime Off: 3 weeks PTO plus 13+ paid company holidays\n\n401(k): Offered (no current employer match, but we are committed to enhancing this benefit in the future)\n\nDue to U.S. export control regulations, we can only hire U.S. Persons (U.S. citizens, Green Card holders, lawful permanent residents, or individuals granted asylum or refugee status). We are unable to provide visa sponsorship or support visa transfers. All work must be performed in the United States.\nBeacon AI is an equal opportunity employer and does not discriminate based on race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected characteristic. 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